DEPARTAMENTO DE ECONOMÍA Pontificia Universidad Católica del Perú DEPARTAMENTO DE ECONOMÍA Pontificia Universidad Católica del Perú DEPARTAMENTO DE ECONOMÍA Pontificia Universidad Católica del Perú DEPARTAMENTO DE ECONOMÍA Pontificia Universidad Católica del Perú DEPARTAMENTO DE ECONOMÍA Pontificia Universidad Católica del Perú DEPARTAMENTO DE ECONOMÍA Pontificia Universidad Católica del Perú DEPARTAMENTO DE ECONOMÍA Pontificia Universidad Católica del Perú DEPARTAMENTO DE ECONOMÍA Pontificia Universidad Católica del Perú DT DECON DOCUMENTO DE TRABAJO EFFECT OF THE JUNTOS SOCIAL PROGRAM ON FEMALE SUPPLY IN PERU Nº 448 Luis García Erika Collantes DEPARTAMENTO DE ECONOMÍA
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DEPARTAMENTO DE ECONOMÍAPontificia Universidad Católica del PerúDEPARTAMENTO DE ECONOMÍAPontificia Universidad Católica del PerúDEPARTAMENTO DE ECONOMÍAPontificia Universidad Católica del PerúDEPARTAMENTO DE ECONOMÍAPontificia Universidad Católica del PerúDEPARTAMENTO DE ECONOMÍAPontificia Universidad Católica del PerúDEPARTAMENTO DE ECONOMÍAPontificia Universidad Católica del PerúDEPARTAMENTO DE ECONOMÍAPontificia Universidad Católica del PerúDEPARTAMENTO DE ECONOMÍAPontificia Universidad Católica del Perú
DEPARTAMENTO DEECONOMÍA
DTDECON
DOCUMENTO DE TRABAJO
EFFECT OF THE JUNTOS SOCIAL PROGRAM ON FEMALE SUPPLY IN PERU
Nº 448
Luis GarcíaErika Collantes
DEPARTAMENTO DEECONOMÍA
DOCUMENTO DE TRABAJO N° 448
EFFECT OF THE JUNTOS SOCIAL PROGRAM ON FEMALE LABOR SUPPLY IN PERU
Luis García Erika Collantes
Diciembre, 2017
DEPARTAMENTO DE ECONOMÍA
DOCUMENTO DE TRABAJO 448 http://files.pucp.edu.pe/departamento/economia/DDD448.pdf
Effect of the Juntos social program on female labor supply in Peru
Luis García1 and Erika Collantes2
Abstract
Over the last ten years, the Peruvian conditional transfer program, Juntos, has expanded
significantly. The objective of this program is to promote school attendance, reduce
child labor, and monitor the growth and development of children in impoverished areas.
Although improvements in these indicators have been appreciated, there may be
unforeseen impacts on the households receiving the transfers. In this study, we focus
on the program’s impact on the working hours of women in recipient households.
According to the standard theory (Becker, 1965), transfers could reduce the labor supply
by assuming leisure to be a normal good. According to theories of family economics,
transfers to one member can affect the allocation of resources to all members of the
household (Chiappori, 1992). The international empirical literature is inconclusive,
pointing to non-existent, negative, and even positive effects (Alzúa, Cruces and Ripani,
2012; Gassman and Trindade, 2016). In the Peruvian case, the most similar study is that
of Fernández and Saldarriaga (2014), who study the impact of the closeness of the
program payment date on female labor supply, finding a negative effect during the week
of payment. Our study goes further, in that: (i) we consider that transfers could result in
a reallocation of resources within the household; (ii) we quantify the changes in working
hours from one year to the next as a result of the program; (iii) in the light of structural
econometric models, we control the main biases (unobservable heterogeneity, selection
bias, and endogeneity), since it is not possible to apply an experimental design. Using
the panel component of Peru’s National Household Survey (Encuesta Nacional de
Hogares del Perú), we find that the Juntos program reduces the paid work of women in
beneficiary households by nine hours per week.
Keywords: Conditional transfer programs, Juntos program, unforeseen impacts,
selection bias, unobservable heterogeneity.
JEL codes: C23, I38, J22
1 Department of Economics, Pontificia Universidad Catolica del Peru ([email protected]) 2 Department of Economics, Pontificia Universidad Catolica del Peru ([email protected]) * A preliminary version of this paper was presented at the conference of the Latin American Studies Association (LASA 2017). The authors are grateful to the participants for their comments.
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Resumen
En los últimos diez años, el Perú ha experimentado una importante expansión del
programa de transferencias condicionadas Juntos. El objetivo de este programa es
incentivar la asistencia escolar, reducir el trabajo infantil y seguir el crecimiento y
desarrollo de los niños en zonas de pobreza. Se han observado mejoras en estos
indicadores; sin embargo, pueden existir impactos no previstos en el hogar que recibe
la transferencia. Este estudio se enfoca en el impacto sobre las horas de trabajo de las
mujeres de hogares beneficiarios. Según la teoría estándar (Becker, 1965), las
transferencias pueden reducir la oferta laboral asumiendo al ocio como un bien normal.
Según las teorías de la economía familiar, las transferencias a un miembro pueden
afectar la asignación de recursos de todos los miembros dentro del hogar (Chiappori,
1992). La literatura empírica internacional no es concluyente, mostrando efectos nulos,
negativos o inclusive positivos (Alzúa, Cruces y Ripani, 2012; Gassman y Trindade, 2016).
En el caso peruano, el estudio más cercano es el de Fernández y Saldarriaga (2014),
quienes estudian el impacto de la proximidad de la fecha de pago del programa Juntos
en la oferta laboral femenina, encontrando un efecto negativo. Nuestro estudio va más
allá porque: (i) considera que las transferencias pueden ocasionar una reasignación de
los recursos dentro del hogar; (ii) cuantifica los cambios en las horas de trabajo de un
año a otro como consecuencia del programa; (iii) a la luz de modelos econométricos
estructurales, controlamos los principales sesgos (heterogeneidad inobservable, sesgo
de selección y endogeneidad), dado que no se puede aplicar un diseño experimental.
Utilizando la Encuesta Nacionales de Hogares Panel del Perú de 2011-2013,
encontramos que el programa Juntos reduce 9 horas semanales el trabajo remunerado
de las mujeres de hogares beneficiarios.
Palabras Clave: Programas de transferencias condicionadas, programa Juntos, Impactos
imprevistos, sesgo de selección, heterogeneidad inobservable.
Códigos JEL: C23, I38, J22
1
Effect of the Juntos social program on female labor supply in Peru
Luis García and Erika Collantes
1. Introduction
In recent years, there has been a significant global expansion in social programs oriented
towards the poorest. Peru has been running a conditional transfer program called Juntos
since 2005, covering the population living in poverty. The objective of this program is to
ensure that school-aged children attend school and undergo periodic health checks. The
transfer is paid to mothers, conditional upon fulfillment of the stipulated program
commitments.
There is a wealth of empirical literature to support the contention that conditional
transfer programs have made a clear impact on school attendance, reducing child labor,
and certain improvements in the field of health and nutrition (Fiszbein and Shady, 2009;
Manley, Gitter and Slavchevska, 2013). However, the doubt remains about whether the
design of such programs might have unforeseen impacts that could either increase and
strengthen household welfare or harm it in some way. One such unforeseen impact is
that on the labor supply of women from beneficiary households.
Standard economic theory predicts that a cash transfer to individuals will decrease their
willingness to work, both in terms of labor participation and hours worked. Family
economics extends the model to propose that transfers may cause a reallocation of
resources, with the impact possibly depending on the bargaining power of individual
household members. As such, we are interested in determining whether the Juntos
program that has been massively implemented over the last decade has made any
impact on the working hours of women from beneficiary households. The female
population is important because of the well-known gender inequality of labor
participation, whereby women participate less than men on the job market and the
elasticity of the female labor supply tends to be greater than the male equivalent given
their traditional role in the household (Kumar and Liang, 2016).
The international empirical literature on this subject is inconclusive, pointing to null,
negative, and even positive effects (Alzúa, Cruces and Ripani, 2012; Gassman and
Trindade, 2016). In the Peruvian case, the closest study to ours is that by Fernández and
Saldarriaga (2014), who analyze the impact of closeness to the program payment date
on the female labor supply and find a negative effect during the week of payment.
Our study goes further than that of Fernández and Saldarriaga (2014), because: (i) we
consider that transfers could result in a reallocation of resources within the household
2
including time spent working; (ii) we use the available data to quantify the changes in
working hours from one year to the next as a result of the program; (iii) in the light of
structural econometric models, we control the main biases (unobservable
heterogeneity, selection bias, and endogeneity); this is because it is not possible to apply
an experimental design, as the program’s allocation was not random but targeted ad
hoc at certain regions and geared towards populations living in poverty. Thus, we will
quantify the causal effect by way of a structural econometric study, because of the lack
of experimental data and because the structural model takes into account decisions
within the households as well as causal relationships, unlike impact evaluation studies
which do not usually feature such analysis (Gaarder et al., 2010).
In this study, we center on the impact on paid work of women. Remunerated work is
known to mean working outside the house, and there are studies that propose that
conditional transfer programs could be encouraging unpaid participation in family
businesses or housework (Streuli, 2012; Escobal and Benites, 2012; Jones et al. 2007).
Thus, it would be interesting to study the impact of these programs on remunerated
work, given the implications of women leaving the job market.
Based on the theoretical literature, we hypothesize that the impact of the Juntos
program on remunerated working hours is negative. The results of our study show that
this impact has indeed been negative and significant, providing evidence of the income
effect on remunerated working hours given an increase in unearned income, or in the
monetary transfer. In this study we do not make a value judgement on whether the
effects we find are good or bad in terms of welfare; we simply present the evidence we
encounter.
The paper is organized as follows: In Section 2 we present a review of the theoretical
and empirical literature on the subject. In Section 3 we set out the econometric
methodology, in which we discuss the main economic problems we come up against and
solve. In Section 4 we define the variables and present a brief analysis of the sample
data. In Section 5 we show the main results. Finally, in Section 6 we conclude.
2. Theoretical framework
2.1. Theoretical literature
Economic literature abounds with studies on the impact of government transfers and
social programs on the labor supply. In this section we break down the theoretical
studies into those in which the decision to work is made on an individual basis (one-
person households); and those in which a person’s labor supply interacts with that of
other members of the household (multi-person households).
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2.1.1. The individual labor supply model
This concerns a basic neoclassical model of labor supply in a single period (see, for
example, Blundell and McCurdy, 1999) in which an person decides between two goods,
one a consumption good and the other leisure time, both of which provide individual
welfare. This person faces two restrictions; one is time-related, where the total amount
of time available is divided between leisure time and time spent in paid work. The other
restriction is budget-related, and corresponds to the spending limit on the consumer
good, which is financed through earned income (hours worked multiplied by wage
earned) plus an unearned income. The microeconomic theory shows that an increase in
unearned income will result in a reduction in the labor supply (Becker, 1965, p. 501).
This model was extended by Gronau (1976), who considered domestic work to be an
additional activity to leisure and work outside the house. Gronau’s model proposes that
given an increase in unearned income, there will be a reduction in hours worked due to
the increase in leisure hours, without affecting domestic work. In the case of individuals
who do not work (that is, those who only engage in domestic work and leisure), the
aforementioned increase will entail an increase in leisure time at the expense of time
spent on domestic work.
However, when it comes to welfare programs, as Moffitt (2002) points out, the outlook
is a little more complex than the basic neoclassical model proposes. First, welfare
programs can take a variety of forms, including conditional or unconditional monetary
transfers, tax relief, and in-kind transfers. Second, these programs are usually applicable
to lower-income population groups. That is, individuals are eligible for programs if they
demonstrate that they meet the requirements for participation, which include having a
low income (mean-tested programs). Moreover, if the earnings of these individuals
increase over time, they may no longer be eligible to participate in these programs. This
creates the possibility that individuals could make decisions to ensure program
eligibility, at their convenience. An important fact that is not taken into account in the
basic model is that many individuals who are eligible for programs may not wish to
participate in them. According to Moffitt (2002), there are two possible rationalizations
for this behavior. One is that there may be a certain degree of stigma attached to
participating in the program, which could reduce individual utility. The other is that
there are costs associated with taking part in these programs (time, effort, “hassle”
costs, etc.), which could discourage individuals from participating.1
In this model, if the monetary transfer to the eligible group is increased, the labor supply
could decrease, because: (a) there could be individuals who decide to work less to
reduce their income in order to become eligible for the program; (b) some who are
already on the program could reduce their working hours due to a pure income effect;
1 In addition, there could be non-convexities in the budgetary restrictions, precisely in the area in which the person could change their eligibility status.
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and (c) eligible individuals who did not previously wish to join the program could change
their minds if any increase in the transfer exceeded the costs of being on the program
(Moffitt, 2002, p.2417).
2.1.2. The family labor supply model
A number of studies have extended the standard one-person model of labor supply to
reflect the fact that there may be more than one member of a household who is willing
to supply working hours on the job market. The simplest way to approach the family
economy is through a family utility-family budget model in which the function of
household utility is a single entity, and depends on the aggregate consumption of all
members and the number of hours worked by each. Moreover, this model assumes that
the income generated through the work of household members goes into a common
purse, used to fund family purchases. Any financial transfers to individual members
would end up forming part of this common purse.
However, various studies have shown that this model is not suitable for the study of
family decisions, whether theoretical or empirical (Chiappori, 1992; Quisumbing and
Maluccio, 1999). For this reason, other models assume different and separate utility
functions for the head of household and his or her spouse, with budgetary restrictions
that are not necessarily common, and family decisions may be influenced by the
individual with the most bargaining power in the household. This has given rise to a
strand known as “collective” whose models contribute to an explanation of how the
allocation of resources are determined within the household. Chiappori (1992) finds a
sharing rule that is endogenously determined in the model, depending on the earnings
of each individual and on non-labor income. This sharing rule determines how a transfer
received can be distributed among the members of a household.
In the light of these models, a cash transfer disbursed to mothers would not necessarily
have the same effect as in the case of the one-person model, since it is possible that a
fraction of the amount transferred will end up in the hands of other household
members. This fraction will depend on the bargaining power of the mother in the
household. It is worth mentioning that the theoretical literature and the empirical
evidence show that the cash transfers received by mothers can alter their
empowerment (Haddad et al., 1997; Barrientos and DeJong, 2004).
2.2. International empirical evidence
In this section we present some international empirical evidence on the impact that
welfare programs have on labor supply. Hoynes (1996), in a study on the United States,
finds that the cash-transfer welfare program Aid to Families with Dependent Children-
Unemployed Parent (AFDC-UP) has a negative effect on labor supply and other work
incentives. For the same country, Krueger and Meyer (2002) evaluate the effects of the
5
social security program on labor supply. Specifically, they focus on workers’
compensation insurance, and find that these programs tend to reduce employees’
working hours.
Gassmann and Trindade (2016) analyze the impact on labor supply of a monetary
assistance program for the neediest in the Kyrgyz Republic. They find that the effect
differs between heads of household and their spouses, the sign being negative for the
former and unclear for the latter (primarily women). The impact on wives varies
depending upon the region of the country and the quarter of the year in which the
questionnaire was conducted.
Among the studies on conditional cash transfer programs, Alzúa, Cruces and Ripani
(2012) investigate the effect of these social programs on incentives to work and the
adult labor supply in three developing countries (Mexico, Nicaragua, and Honduras). To
this end, they use a differences-in-differences technique since they have experimental
data sets from those programs. The results show the effect of these social programs on
the adult labor supply has been negative for the most part, but small and non-significant.
However, in the case of the Mexican program Progresa, they find a small and positive
impact on the number of hours worked by recipient mothers.
The literature on conditional cash transfers also includes that of Molyneux (2007) who,
after studying Mexico’s Progresa/Oportunidades, proposes that such programs, rather
than empowering women, limit their traditional role as homemaker, childcarer, and
administrator of household spending, while their male partners retain their role as
breadwinner.
2.3. Description of the Juntos program
Before presenting the literature review on the Peruvian case, it is important to introduce
the Juntos program. Since 2005, Peru has been part of the group of countries that has
implemented conditional cash transfer programs to benefit children. The Juntos
program consists fundamentally of the disbursement of a cash transfer (200 soles
bimonthly, approximately 60 American dollars) to women with young children,
conditional upon their attendance of school and on growth and development checks.
The program began in the deprived region of Ayacucho, which was the cradle of the
political violence that plagued Peru during the 1980s and part of the 1990s.
Then, throughout 2005, the program was rolled out in just five impoverished regions of
the country. In the subsequent years it was extended to most of Peru, with coverage in
21 of its 26 political regions by 2017. Expansion has been gradual and the program does
not cover these regions in their entirety, focusing primarily on deprived rural areas.
Internally, in each region the program has reached the provinces and districts with the
6
highest levels of poverty and need. According to statistics from the third quarter of 2017,
the program contains 742,094 registered households, of which 674,607 receive the
transfer regularly. In these households, there are 1,599,582 beneficiary children.
A notable fact about this program is that no base line data was collected prior to
implementation. Moreover, the initial selection of beneficiary districts was not based
solely on poverty criteria, but also included those districts that were affected by the
internal armed conflict. In subsequent years beneficiary selection was sequential,
focusing primarily on the regions with the highest poverty levels in certain districts;
within these districts, the Household Targeting System conducted a joint assessment
with the local authorities to determine the recipient households.
This methodology meant that the information obtained from the program was far from
being an experimental design. In the first place, the beneficiary selection was not
random but determined by the authorities of the day, without a statistically comparable
control group. Moreover, the participation of local authorities in the implementation
and selection of beneficiaries introduced specific characteristics of each region (for
example, preferences and acceptance of state policies by the population) to the
selection process. Finally, the participation of each household in the program is
voluntary, not compulsory. This fact creates a self-selection process which is based on
subjective criteria (costs and benefits of taking part). All of these considerations suggest
the existence of relevant information on individual participants that is not normally
available to program evaluators. Therefore, given the nature of the data, measurement
of the effect of Juntos requires statistical and econometric techniques that take into
account the possible biases stemming from the program’s design and the omitted
variables.
2.4. Empirical evidence in Peru
In a qualitative study on the effects of the program in the Ayacucho region, Streuli
(2012) finds similar results to those of Molyneux (2007) regarding the traditional rule of
women, but she also points to evidence of empowerment of beneficiary women in terms
of their self-esteem and social image. Moreover, she proposes that some beneficiary
women could use the funds to undertake or bolster habitual agricultural activities.
Moreover, because participating children spend more time at school, mothers reallocate
their time to domestic activities.
In a qualitative analysis, Escobal and Benites (2012) use a differences-in-differences
matching method to find that the Juntos program has had an impact on the allocation
of time to child labor. According to their calculations, which draw on the “Young Lives”
database, the number of minutes per day that children assign to remunerated activity is
reduced slightly, while the minutes allotted to unpaid work increase. The authors infer
from this that the program is fostering adult work (non-remunerative for the mothers)
7
in family businesses. Jones et al. (2007) reach similar conclusions about the increase in
the workload of women in agricultural and domestic activities.
In another study, Fernández and Saldarriaga (2014) attempt to estimate the impact of
Juntos on hours worked through the effect that closeness to the payment date might
have. Using the differences between the Juntos payment dates and those of the
interview for the National Household Survey (Encuesta Nacional de Hogares, ENAHO)
within a single municipality, they find that the Juntos program reduces the number of
hours worked by between six and ten hours during the week following the cash transfer
date. The authors attribute this impact on hours worked to the time it takes to access
the money from the transfer, the time allocated to spending it, and a possible income
effect. This is likely to be a very short-term impact, as it only considers the week that
immediately follows each payment date. This drop in working hours is greater for
married women and for mothers with children below the age of five. Moreover, they do
not find a significant effect on workforce participation in remunerated activities, nor on
the labor supply of the husbands of married women.
Finally, Alencastre and Del Pozo (2017) estimate the impact of Juntos on labor
participation. Using a linear and non-linear double differences model, they do not find
statistically significant evidence of changes to labor participation in the years 2004 and
2014. In another study, Del Pozo and Guzmán (2010), employ quasi-experimental
designs to find that Juntos is encouraging agricultural production activities in rural
households, from which they infer that if might affect remunerated working hours.
3. Econometric methodology
We will assess the impact of the Juntos conditional transfer on the female labor supply
through an econometric model that considers working hours as the endogenous
variable, and has participation in the Juntos program as the main regressor. In addition,
to control for the influence of other factors, the regression includes control variables.
As has been established in the literature on labor participation, econometric variables
of this type can be subject to selection bias, and occurs in this case when the hours
worked and wages are only observable for women who have opted to work. If this
phenomenon were to materialize, the model could not be estimated using ordinary least
squares (OLS), as this would present bias or inconsistency in the estimators, but using
maximum likelihood estimation (MLE), following the model proposed by Heckman
(1979).
Another econometric problem occurs when the error in the equation presents an
unobserved heterogeneity component, such as individual tastes and presences related
8
to work, that is correlated to the decision to participate in the program. If this were to
occur, estimation using the Heckman method would be biased and inconsistent. On the
other hand, if a panel data is employed, the bias due to unobservable heterogeneity
could be eliminated through a fixed effects approach, since the first difference or the
within groups estimator would eliminate this component. However, the fixed effects
model generally does not eliminate selection bias unless the conditional expectation of
the error given participation on the labor market remains constant over time, which
need not necessarily be the case.
A third econometric problem arises if the regressor of interest is endogenous, or if there
are omitted variables that change over time, or this regressor has been measured with
error. As a consequence, a correlation between the regressor and the idiosyncratic error
is produced, which makes it necessary to use an alternative based on the instrumental
variables method. Any other method could give rise to estimations with endogeneity
bias. In the case of Juntos, this problem is likely to exist given that participation in the
program is not random but voluntary and subject to the policy decisions of the program
administrators and local authorities.
Fortunately, there are techniques that allow these biases to be controlled for. To go into
these techniques in detail, we employ the following sample selection model with panel
data,
𝑦𝑖𝑡∗ = 𝑥𝑖𝑡𝛽 + 𝛼𝑖 + 𝜀𝑖𝑡 (1)
𝑑𝑖𝑡∗ = 𝑧𝑖𝑡𝛾 + 𝜂𝑖 + 𝑢𝑖𝑡 (2)
𝑑𝑖𝑡 = 1[𝑧𝑖𝑡𝛾 + 𝜂𝑖 + 𝑢𝑖𝑡 > 0] (3)
𝑦𝑖𝑡 = 𝑑𝑖𝑡 ∙ 𝑦𝑖𝑡∗ (4)
Equation (1) 𝑦𝑖𝑡∗ represents the desired working hours of each individual 𝑖 at the moment
𝑡 and is a latent variable that is unobserved in the entire sample. In Equation (2), 𝑑𝑖𝑡∗ is
another continuous latent variable that determines the participation of an individual 𝑖
in the labor market. This latter variable is unobservable, as there is only a dummy
variable that takes the value of 1 for the individual 𝑖 at the moment 𝑡 if the right side of
Equation (2) is positive and 0 if the opposite occurs (Equation [3]). Finally, Equation (4)
shows that the observed hours worked 𝑦𝑖𝑡 coincide with the desired hours if they are
strictly positive, and equal to 0 otherwise. In equations (1) and (2), the unobserved
heterogeneity error components are 𝛼𝑖 and 𝜂𝑖, which do not vary over time, and capture
elements specific to the individuals that do not usually change over time, such as tastes,
preferences, habits, customs, etc. Meanwhile, the idiosyncratic errors 𝜀𝑖𝑡 and 𝑢𝑖𝑡
behave like typical errors in linear regression models. Finally, the vectors 𝑥𝑖𝑡 and 𝑧𝑖𝑡
contain variables that determine the above-mentioned endogenous variables.
9
This is a model with sample selection if there is some kind of covariance between the
idiosyncratic errors in equations (1) and (2) – that is, 𝐶𝑜𝑣(𝜀𝑖𝑡, 𝑢𝑖𝑡) ≠ 0, and hence
Equation (1) cannot be estimated using OLS. On the other hand, if the unobservable
heterogeniety term 𝛼𝑖 has a correlation with our main regressor included in the
vector 𝑥𝑖𝑡, 𝐶𝑜𝑣(𝛼𝑖 , 𝑥𝑖𝑡) ≠ 0, then the OLS and Heckman estimations in Equation (1) will
present bias. Finally, endogeneity bias will arise if 𝐶𝑜𝑣(𝑥𝑖𝑡 , 𝜀𝑖𝑡) ≠ 0.
Below we present the three estimators proposed in the literature to resolve these
problems.
3.1. The Kyriazidou's estimator
It was developed in Kyriazidou (1997), and the objective of this estimator is to eliminate
bias due to unobserved heterogeneity and selection bias in a single procedure. As
mentioned, the differentiation procedures that eliminate the heterogeneity bias do not
necessarily eliminate the sample selection bias. However, according to Kyriazidou’s
strategy, if one person participates in two consecutive periods, the magnitude of this
selection effect is very similar in both periods if 𝑧𝑖𝑡 does not change over time. Let 𝜆𝑖𝑡 be
this effect2. Then, Equation (1) can be rewritten as
𝑦𝑖𝑡 = 𝑥𝑖𝑡𝛽 + 𝜆𝑖𝑡 + 𝛼𝑖 + 𝑣𝑖𝑡 (1’)
where 𝑣𝑖𝑡 ≡ 𝜀𝑖𝑡 − 𝜆𝑖𝑡, which has zero mean. The term 𝜆𝑖𝑡 in (1’) is the equivalent of the
term of correction introduced in the sample selection models (inverse Mills ratio), but it
is not constant in time, so the first differences estimator does not eliminate it.
Nevertheless, under an assumption called “conditional exchangeability” and assuming
𝑧𝑖1𝛾 = 𝑧𝑖2𝛾 it will be the case that 𝜆𝑖1 = 𝜆𝑖2. Then, the calculation of the first difference
will also eliminate the effect of selection bias. In empirical terms, (a) it is most probable
that 𝛾 will be unknown, and, (b) in general, 𝑧𝑖1𝛾 ≠ 𝑧𝑖2𝛾. To resolve (a), Kyriazidou’s
method estimates equations (2) and (3) using a conditional logit fixed effects model,
obtaining �̂� and 𝑧𝑖𝑡�̂� for 𝑡 = 1, 2. For (b), Kyriazidou assigns higher weights (kernel
weights) to those observations for which 𝑧𝑖𝑡�̂� ≅ 𝑧𝑖𝑡−1�̂�, and estimates equation (1’) in
first differences using weighted least squares for the case of 𝑑𝑖𝑡𝑑𝑖𝑡−1 = 1. When the
panel has more than two periods, the differences between two contiguous periods or
with more years of difference can be calculated. This will be the case of our estimator,
since our 3-year-panel database is balanced. Let �̂�𝑖𝑛 be the estimated weight, then the
In which the terms 𝜆𝑖𝑡𝑠 and 𝜆𝑖𝑠𝑡 are the selection bias correction terms and are a function
of the exogenous variables from Equation (2) in both periods and of the respective
parameters.3 The empirical strategy of Rochina-Barrachina involves estimating, in a first
stage, the joint decision of labor participation in the periods 𝑡 and 𝑠 using a bivariate
probit model, which takes the vectors 𝑧𝑖𝑡 of all periods as regressors. These estimations
are used to construct the estimations �̂�𝑖𝑡𝑠 and �̂�𝑖𝑠𝑡, which are introduced in (6). Then,
this equation is estimated using OLS, and in our case we estimate the standard
deviations using the bootstrap technique. It is worth noting that this technique entails
estimations taking the periods two-by-two. Using a T-period panel sample, (𝑇2
) =𝑇!
2!(𝑇−2)!
3 See Rochina-Barrachina (1999) for the exact formulas.
11
estimations are produced. In our case, we work with a three year panel so have three
estimations that can be integrated into a single more efficient result by using a distance
minimization procedure, with the variances-covariance matrix as a weighting matrix.
3.3. The estimator of Semykina and Wooldridge (2010)
The previous two estimators may not be suitable if the regressor of interest in 𝑥𝑖𝑡 is
correlated to the idiosyncratic error 𝜀𝑖𝑡. As we explained earlier, if this occurs with the
Juntos program, the estimators of Kyriazidou and Rochina-Barrachina would be biased
and inconsistent. Semykina and Wooldridge (2010), extending the model of Woodridge
(2005), propose an instrumental variables estimator to estimate this kind of model, in
which there is unobserved heterogeneity correlated to the regressor of interest,
selection bias, and endogeneity of the regressor of interest.
The Semykina and Wooldridge’s model is in the spirit of Mundlak's (1978) correlated
random effect model, which obtains the fixed-effect estimations of Equation (1), by
adding time averages of a vector of variable 𝑧𝑖𝑡. This vector includes the exogenous
regressors in the vector 𝑥𝑖, plus other valid instruments that are included in Equation (2)
but not in (1). Then, Equation (1) is converted into
𝑦𝑖𝑡 = 𝑥𝑖𝑡𝛽 + 𝑧�̅�𝜔 + 𝐸[𝑣𝑖𝑡1|𝑧�̅�, 𝑑𝑖𝑡] + 𝑒𝑖𝑡
where 𝛼𝑖 = 𝑧�̅�𝜔 + 𝑐𝑖, and 𝑣𝑖𝑡1 = 𝑐𝑖 + 𝜀𝑖𝑡. Defining 𝑣𝑖𝑡2 = 𝜂𝑖 + 𝑢𝑖𝑡, under the typical
assumption4 that 𝑣𝑖𝑡1 is a linear function of 𝑣𝑖𝑡2 of the form 𝐸[𝑣𝑖𝑡1|𝑣𝑖𝑡2] = 𝛾𝑣𝑖𝑡2, it
follows that 𝐸[𝑣𝑖𝑡1|𝑧�̅�, 𝑑𝑖𝑡] = 𝛾𝐸[𝑣𝑖𝑡2|𝑧�̅�, 𝑑𝑖𝑡], in which 𝐸[𝑣𝑖𝑡2|𝑧�̅�, 𝑑𝑖𝑡] = 𝜆𝑖𝑡 is the
equivalent of the inverse Mills ratio as a selection bias term of correction. Then,
Semykina and Wooldridge estimate the model
𝑦𝑖𝑡 = 𝑥𝑖𝑡𝛽 + 𝑧�̅�𝜔 + 𝛾𝜆𝑖𝑡 + 𝑒𝑖𝑡 (7)
using two-stage least squares for pooled data. In the first stage, 𝜆𝑖𝑡 is estimated through
probit for each period. Then, �̂�𝑖𝑡 is used in (7), which is estimated using as instrumental
variables the valid instruments from Equation (2), 𝑧�̅� and �̂�𝑖𝑡. The standard deviations
are calculated using the bootstrap technique.
4. The data and the variables
We estimate the model primarily through the ENAHO panel for the years 2011-2013.
This survey has nationwide coverage, including both urban and rural areas, and collects
detailed information about household characteristics, members, and primary economic
activities (job, education, healthcare, social programs, etc.). We selected the afore-
4 This assumption is met when the joint distribution of errors is normal.
12
mentioned years because the Juntos panel was expanded considerably between 2011
and 2013, and these variations are captured in the survey.
Because we are interested in the labor behavior of women, we consider only those aged
14 or over. In the three years selected, it can be observed that some of these women
can opt not to participate in the labor market during a given year. Some interesting
patterns are discerned in this regard, in that the work engaged in by the women may
involve dependent or independent activities; moreover, it is common in Peru for some
women to dedicate time to unpaid work in family businesses.
Taking the above into account and discarding certain inconsistent data in the panel
dataset,5 the total sample from the 2011-2013 ENAHO Panel survey is 5,632 women
observed over the three years. Under the methodologies of Kyriazidou and Rochina-
Barrachina, the estimations are limited to the case of women working during at least
two of the three periods, so the number of observations may decrease. In the case of
the Semykina and Wooldridge estimator, those women in the sample who have worked
at least one out of the three years are taken into account, so the size is somewhat larger.
The endogenous variable of the main equation is hours engaged in a remunerated
economic activity in the last seven days.6 This variable assigns a value of 0 to those
individuals who undertake unpaid work or do not participate in any economic activity.
The endogenous variable in the selection equation is a dummy variable that represents
participation in the remunerated labor market, and takes a value of 1 if the individual
presents a positive number of hours worked over the last seven days, and of 0 otherwise.
Meanwhile, the regressor of interest is a dummy variable that indicates whether the
household participated in the Juntos program at the time of the survey. In addition,
some control variables are added to the model.
Some of the variables taken from the ENAHO survey and used in the model are:
Household income: The annual income of the household in soles.
Years of schooling: The years of education completed by the individual.
Age: The age of the person in years.
Number of children: The number of children below the age of six in the
household.
Married: A dummy variable that takes the value of 1 if the woman is married,
and 0 otherwise.
5 Fundamentally, these were consistencies in relation to age and sex. 6 It should be noted that a sizable percentage of female workers in Peru engage in unremunerated economic activities. For example, according to our calculations using cross-sectional ENAHO data from 2011, of the total female participants in the survey that year who engaged in some type of economic activity, the main pursuit of 32.57% was unpaid family work. On the other hand, in that same year just 12% of men in the survey undertook unpaid work as their main activity.
13
Chronic illness: Dummy variable that takes the value of 1 if the individual suffers
some form of chronic illness, and of 0 if the individual does not suffer from any
illness.
Pregnancy: Dummy variable equal to 1 if the woman received pregnancy checks
over the last 12 months, and of 0 otherwise.
Illness of a member: Dummy variable equal to 1 if a household member
experienced a serious illness or accident, and of 0 otherwise.
Desertion by head of household: Dummy variable equal to 1 if the head of
household deserted the household, and of 0 otherwise.
Crime: Dummy variable equal to 1 if the household was victim to any form of
crime (theft, assault, etc.), and to 0 otherwise.
Natural disaster: Dummy variable equal to 1 if the household was victim to any
form of natural disaster (drought, storm, plague, etc.), and to 0 otherwise.
Table 1 presents the descriptive statistics of the main endogenous variable, working
hours in the primary paid activity. The table shows that an average of 17 hours per week
were allotted to paid work for the three years from 2011 to 2013. This average also takes
into account the zero hours of non-working women; if only working women are taken
into account, the average number of hours per week increases to 36. This is because the
sample is approximately halved when only working women are factored in, in
comparison with the sample that includes both women who work and those who do not
work. It is also noteworthy that in both samples there is variability between individuals
and over time.
Table 2 sets out the descriptive statistics of the other variables used in the estimations.
As can be seen in the table, female labor participation in remunerated activities
fluctuated around 48% in the years 2011-2013. It is also notable that just 11% of women
participated in Juntos over the same period. Another striking result is that 49% of
women in the sample report having or suffering from a chronic illness. Moreover, 45%
of women were found to live in households with children below the age of six, while the
8 It is worth mentioning that the estimation of the “proxy meanscore” variable is not an excluded variable per se, so we do not consider it into the evaluation of the validity of the excluded variables. See Semykina and Wooldridge (2010), page 378 for an explanation of its function. The only variables taken into account are the HDI, percentage with higher education, and the respective averages in time.
17
As stated in the methodology section, we perform the estimation of the working hours
model following the approaches of Kyriazidou (1997), Rochina-Barrachina (1999), and
Semykina and Wooldridge (2010) for the Juntos program.
The estimations of the working hours model for Juntos using the standard “within
groups” fixed-effects method, as well as the three above-mentioned methods, are set
out in Table 4. The results show that the impact of Juntos on women’s working hours in
Peru is negative and significant for 2011-2013, which is consistent with the economic
theories. Under the Kyriazidou and Rochina-Barrachina methods, Juntos reduces the
remunerative activities of women over the age of 14 by an average of between three
and four hours per week. However, the impact is greater under the method of Semykina
and Wooldridge, which does take into account the problems of exogeneity associated
with the Juntos variable. Under this method, it is estimates that Juntos reduces
participation in remunerated activities by 9.4 hours per week, on average. It is possible
that the Kyriazidou and Rochina-Barrachina methods are subject to an attenuation
effect since they do not control the exogeneity of Juntos.
As stated in earlier sections, the international literature has also reported negative
effects of cash transfers on hours worked (for example, Hoynes, 1996; Krueger and
Meyer, 2002), and those results differ from the findings of Alzúa et al. (2012) regarding
conditional transfers. If we take the estimation of an effect of 9.4 hours less hours
worked as the most accurate, this impact is close to that found by Fernández and
Saldarriaga (2014), calculated at between six and ten hours, although the impact of
these authors is very short term. In our work, the calculation can be understood as a
change from one year to the next, and thus implies readjustments in internal household
resources, which would explain the slightly greater impact. On the other hand, this
negative impact is consistent with that cited by other authors, such as Escobal and
Benites (2012), who point out that in the case of children, remunerated work decreases
in favor of more time spent on unremunerated activities. In our study, a decrease is also
observed in the remunerated work of women. There is also consistency with the findings
of the qualitative study by Streuli (2012), who demonstrates that the Juntos transfer is
occasionally used to strengthen family businesses in which women perform unpaid
work, but also notes that there may have been an increase in the time spent on non-
laboral activities. Moreover, the effect identified is also consistent with the hypothesis
that sending children to school results in a loss of unremunerated hours spend in the
family business. These unpaid working hours would have to be covered by other
household members, in this case by women, which would lead to a drop in their time
spent in remunerated work. Our results are also consistent with the findings of Del Pozo
and Guzmán (2010), who find that the Juntos program has a positive impact on
household production, which could serve to reduce women’s participation in paid work
outside the house.
18
As to the other control variables, in the estimation it can be observed that the pregnancy
variable presents a negative and significant sign under the Semykina-Wooldridge
method, such that if a woman is pregnant, the number of hours worked per week
reduces by four; this effect is reasonable. Curiously, under the Kyriazidou method, a
positive sign is presented, which is counter-intuitive, and under Rochina-Barrachina the
impact is non-significant. On the other hand, the estimations show that the variable for
the number of children aged less than six presents a negative sign across all methods,
but is only significant under the Kyriazidou and Semykina-Wooldridge approaches. It is
notable that this effect is relatively small; remunerated work is reduced by one hour and
a half for each additional small child in the household. Meanwhile, if a woman suffers
from some form of chronic illness, her working hours will decrease slightly, although the
effect proves to be significant only under the Kyriazidou method. Similarly, if a member
of the household suffers from a serious illness, the number of working hours will
decrease, but on this occasion the impact is only significant, to 10%, under the method
of Semykina-Wooldridge. Moreover, if the head of household deserts the household,
the average number of hours women spend working increases by almost nine, though
this effect is only significant under the Kyriazidou method. Finally, there is a marked
difference in the hours spent in paid activities among women who live in rural areas
versus their urban counterparts.
19
Table 4
Estimation of impact of Juntos program on hours worked in remunerated activities (Panel 2011-2013)
Variable
Fixed effects conditioned
to d=1
Kyriazidou
(1997)
Rochina-Barrachina
(1999)
Semykina –Wooldridge
(2010)
Juntos -3.798 ** -3.846 *** -3.076 * -9.471 **
(1.819) (1.009) (1.773) (4.108)
Family income 38.27x10-5 ** 173.63x10-5 *** 43.42x10-5 85.34x10-5 ***
Robust standard errors in parentheses. ***=sig. at 1%, **=sig. at 5%, *=sig. at 10%. Note 1: Rochina-Barrachina and Semykina-Wooldridge estimations use bootstrap standard errors. Note 2: The selection bias terms of correction in the Rochina-Barrachina and Semykina-Wooldridge regression do not prove to be significant (not shown in the table). Compilation: authors.
It is worth noting the results of the validity of the instruments. For this, we have opted
for the well-known F-test for the strength of the instruments, the Sargan test for
overidentifying restrictions, and the Hausman test for the pertinence of using the
instrumental variables method based on evaluation of the endogeneity of the Juntos
regressor. These tests only apply to the regression with instrumental variables under
Semykina and Wooldridge’s method. Table 5 shows the validity test for the instruments.
As can be seen the chosen instruments meet the required conditions. As to the strength
of the instruments, the F statistic applied to the excluded instruments is equal to 116.1,
exceeding the known “golden rule” that sets the minimum threshold at ten units.
According to the Sargan test for overidentifying restrictions, the null hypothesis is not
20
rejected, so it is correct to exclude the afore-mentioned instruments from the
regression. Finally, the Wu-Hausman test of endogeneity in the Juntos variable indicates
that the null hypothesis of non-endogeneity is rejected for this variable; thus, the
estimation by instrumental variables is preferred to an estimation using ordinary least
squares.
Table 5 Assessment of instrument validity
Edogeneity Instrument weakness Restrictions on identifiers
H0: Exogenous regressors H0: Weak instruments H0: Instruments correctly excluded from the main
equation
Wu-Hausman 4.351 Strength of excluded variables
116.141 Sargan 2.587
P-value 0.037 P-value 0.000 P-value 0.460
Compilation: authors.
Finally, we evaluate the interaction of the program’s impact with some of the regressors.
To this end, we have performed separate estimations for each population subgroup. We
present these results in Table 6. First, we see clearly that there is a considerable
difference in the program's impact depending on women's marital status. Thus, for the
group of married women, Juntos has no appreciable significant effect on remunerated
working hours, while for unmarried women, if a member of their household is a Juntos
beneficiary, this will reduce their engagement in paid work by 15 hours per week.
Another striking result is the difference found according to the age of the women. For
women below the age of 40, the Juntos program reduces their working time by almost
18 hours per week, while there is practically no effect for those over the age of 40. This
result suggests that younger women are more sensitive in their labor participation,
perhaps because they are involved in other alternative activities, or because they have
less economic responsibilities within the household. As to the other variables in Table 6,
there are practically no differences in subpopulations, or the estimations are not entirely
reliable since the instruments do not achieve validity in these subpopulations.
21
Table 6 Impact of Juntos on hours worked per week, by
population groups
Juntos coefficient P-value
Unmarried -15.451 0.002
Married 3.308 0.684
Without children < 6 years -7.746 0.180+
With children < 6 years -10.637 0.078
Age <40 -16.630 0,003
Age > 40 -0.834 0.896
No chronic illness -10.085 0.053
Chronic illness -9.436 0.218
Rural -6.179 0.060+
Urban -17.770 0.250 + Instruments do not prove valid
6. Conclusions
In this study we investigated the impact of the Juntos program on the number of hours
that women in Peru spend in remunerated work. Economic theory holds that this effect
should be negative due to the income effect that cash transfers bring about. Because
the Juntos program does not yield experimental data and the quasi-experimental
techniques do not control all biases, it is necessary to employ a structural econometric
model that controls for unobserved heterogeneity biases, selection bias, and regressor
endogeneity. We used techniques developed specifically to control for these biases,
such as those of Kyriazidou (1997), Rochina-Barrachina (1999), and Semykina and
Wooldridge (2010).
Our results for Peru show that the impact of the Juntos conditional cash transfer
program on hours spent in remunerated activities has been negative and significant,
evidencing the income effect on working hours in the case of a cash transfer.
We found an average estimated impact of 9.4 hours per week less remunerated work
for those women whose households are recipients on the Juntos program. These results
are close to those found for the very short term by Fernández and Saldarriaga (2014),
and to other authors who present negative results. However, the effect found in this
paper is not a short-term effect, but interpreted as an annual change. Our results are in
keeping with the theories on intra-household resource allocation. Moreover, we find
22
significant differences in impact based on women’s marital status and age, with the
impact greater in the case of young and of unmarried women.
The results leads us to assert that the Juntos program should not be seen purely as a
program to benefit children, but one that also has implications for other household
members, principally on women, given the reallocation of time and other household
resources. Whether the program is expanded or continued, these collateral effects
should be kept in mind, and the results should also be considered alongside other
studies that have evaluated unforeseen or indirect effects of the program for an overall
evaluation of its positive and negative aspects.
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ÚLTIMAS PUBLICACIONES DE LOS PROFESORES DEL DEPARTAMENTO DE ECONOMÍA
Libros Adolfo Figueroa
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Perú. Mario D. Tello 2017 La productividad total de factores agregada en el Perú. Nacional y Departamental.
Lima, Instituto Nacional de Estadística e Informatica. Félix Jiménez 2017 Veinticinco años de modernización neocolonial: Críticas de las políticas neoliberales
en el Perú. Lima, Instituto de Estudios Peruanos. Carlos Contreras y Elizabeth Hernández (editores) 2017 Historia económica del norte peruano. Señoríos, haciendas y minas en el espacio
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y del Caribe para el Desarrollo Humano y el Enfoque de Capacidades. Lima, Fondo Editorial, Pontificia Universidad Católica del Perú.
Waldo Mendoza y Janneth Leyva 2017 La economía del VRAEM. Diagnósticos y opciones de política. Lima, USAID-CIES. Félix Jiménez 2017 Macroeconomía. Enfoques y modelos. Lima, Editorial Macro. Máximo Vega-Centeno 2017 Ética y deontología. Lima, Fondo Editorial, Pontificia Universidad Católica del Perú.
Documentos de Trabajo No. 447 “El derecho a la educación intercultural bilingüe y la política fiscal en Perú”. Luis
García y Erika Collantes, Noviembre, 2017. No. 446 “Cambio técnico en el sector regulado de la microfinanzas peruanas: 2003-2015”.
Giovanna Aguilar y Jhonatan Portilla. Octubre, 2017. No. 445 “Teaching Modern Macroeconomics in the Mundell-Fleming Language: The IS-
MR-UIP-AD-AS Model”. Waldo Mendoza Bellido. Setiembre, 2017. No. 444 “La experiencia de la Banca de Desarrollo en el Perú: 1990-2015”. Oscar
Dancourt Masías y Renzo Jiménez Sotelo. Agosto, 2017. No. 443 “Teaching Modern Macroeconomics in the Traditional Language: The IS-MR-AD-
AS Model”. Waldo Mendoza Bellido. Julio, 2017. No. 442 “Has the Gender Wage Gap been Reduced during the ‘Peruvian Growth Miracle?’
A Distributional Approach”. Juan Manuel del Pozo Segura. Julio, 2017. No. 441 “Crecimiento y desindustrialización prematura en Perú. Un análisis Kaldoriano”.
Félix Jiménez. Junio, 2017. No. 440 “La economía de PPK. Promesas y resultados: la distancia que los separa”. Waldo
Mendoza Bellido y Erika Collantes Goicochea. Mayo, 2017. No. 439 “¿Es la educación un mecanismo de salida hacia mejores ocupaciones para las
trabajadoras del hogar?” Cecilia Garavito. Mayo, 2017. Materiales de Enseñanza No. 2 “Macroeconomía: Enfoques y modelos. Ejercicios resueltos”. Felix Jiménez.
Marzo, 2016. No. 1 “Introducción a la teoría del Equilibrio General”. Alejandro Lugon. Octubre, 2015.
Departamento de Economía - Pontificia Universidad Católica del Perú Av. Universitaria 1801, Lima 32 – Perú.
